dfrr: Dichotomized Functional Response Regression
In asgari-fatemeh/dfrr: Dichotomized Functional Response Regression

Description Usage Arguments Details Examples

Implementing Function-on-Scalar Regression model, in which the response function is dichotomized and observed sparsely.

dfrr(
  formula,
  yind = NULL,
  data = NULL,
  ydata = NULL,
  method = c("REML", "ML"),
  rangeval = NULL,
  basis = NULL,
  times_to_evaluate = NULL,
  ...
)

`formula`	an object of class "`formula`" (or one that can be coerced to that class with `as.formula`: a symbolic description of the model to be fitted.
`yind`	a vector with length equal to the number of columns of the matrix of functional responses giving the vector of evaluation points (t_1,...,t_{G}). If not supplied, `yind` is set to `1:ncol(<response>)`.
`data`	an (optional) `data.frame` containing the covariate data. the variable terms will be searched from the columns of `data`, covariates also can be read from the workspace if it is not available in `data`.
`ydata`	an (optional) `data.frame` consists of three columns `.obs`, `.index` and `.value`, supplying the functional responses that are not observed on a regular grid. ydata must be provided if the sampling design is irregular.
`method`	detrmines the estimation method of functional parameters. Defaults to "`REML`" estimation.
`rangeval`	an (optional) vector of length two, indicating the lower and upper limit of the domain of latent functional response. If not specified, it will set by minimum and maximum of `yind` or `.index` column of `ydata`.
`basis`	an (optional) object of class `'basisfd'`. Defaults to cubic bspline basis.
`times_to_evaluate`	a numeric vector indicating the set of time points for evaluating the functional regression coefficients and principal components.
`...`	other arguments that can be passed to the inner function `AMCEM`.

The output is a dfrr-object, which then can be injected into other methods/functions to postprocess the fitted model, including: coef.dfrr,fitted.dfrr, basis, residuals.dfrr, predict.dfrr, fpca, summary.dfrr, model.matrix.dfrr, plot.coef.dfrr, plot.fitted.dfrr, plot.residuals.dfrr, plot.predict.dfrr, plot.fpca.dfrr

set.seed(2000)
N<-50;M<-24

X<-rnorm(N,mean=0)
time<-seq(0,1,length.out=M)
Y<-simulate_simple_dfrr(beta0=function(t){cos(pi*t+pi)},
                        beta1=function(t){2*t},
                        X=X,time=time)

dfrr_fit<-dfrr(Y~X,yind=time)

plot(dfrr_fit)

##### Fitting dfrr model to the Madras Longitudinal Schizophrenia data
data(madras)
ids<-unique(madras$id)
N<-length(ids)

ydata<-data.frame(.obs=madras$id,.index=madras$month,.value=madras$y)

xdata<-data.frame(Age=rep(NA,N),Gender=rep(NA,N))
for(i in 1:N){
  dt<-madras[madras$id==ids[i],]
  xdata[i,]<-c(dt$age[1],dt$gender[1])
}
rownames(xdata)<-ids


madras_dfrr<-dfrr(Y~Age+Gender+Age*Gender, data=xdata, ydata=ydata, J=11)
coefs<-coef(madras_dfrr)
plot(coefs)

fpcs<-fpca(madras_dfrr)
plot(fpcs)
plot(fpcs,plot.eigen.functions=FALSE,plot.contour=TRUE,plot.3dsurface = TRUE)

par(mfrow=c(2,2))
fitteds<-fitted(madras_dfrr) #Plot first four fitted functions
  plot(fitteds,id=c(1,2,3,4))

resids<-residuals(madras_dfrr)
plot(resids)


newdata<-data.frame(Age=c(1,1,0,0),Gender=c(1,0,1,0))
  preds<-predict(madras_dfrr,newdata=newdata)
  plot(preds)


newdata<-data.frame(Age=c(1,1,0,0),Gender=c(1,0,1,0))
newydata<-data.frame(.obs=rep(1,5),.index=c(0,1,3,4,5),.value=c(1,1,1,0,0))
preds<-predict(madras_dfrr,newdata=newdata,newydata = newydata)
plot(preds)